Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques. In particular, a deep Convolutional Neural Network (CNN) is used to extract spatial features in an unsupervised...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Prognostics and Health Management Society
2016-12-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2460 |